US7634367B1 - Estimating fluidic properties and using them to improve the precision/accuracy of metered fluids and to improve the sensitivity/specificity in detecting failure modes - Google Patents
Estimating fluidic properties and using them to improve the precision/accuracy of metered fluids and to improve the sensitivity/specificity in detecting failure modes Download PDFInfo
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- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F22/00—Methods or apparatus for measuring volume of fluids or fluent solid material, not otherwise provided for
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- the present invention relates to improving the precision and/or accuracy of fluids aspirated or dispensed.
- the present invention relates to automated diagnostic analyzers having improved aspirate or dispense accuracy and/or precision.
- the present invention also relates to having improved sensitivity/specificity in detecting failure modes.
- diagnostic analyzers include immunodiagnostic analyzers such as the Vitros® ECi immunodiagnostic analyzer, or clinical chemistry analyzers such as the Vitros® 5.1 FS, both sold by Ortho-Clinical Diagnostics, Inc. All such analyzers are collectively called diagnostic analyzers.
- immunodiagnostic analyzers such as the Vitros® ECi immunodiagnostic analyzer, or clinical chemistry analyzers such as the Vitros® 5.1 FS, both sold by Ortho-Clinical Diagnostics, Inc. All such analyzers are collectively called diagnostic analyzers.
- Such systems rely on the assurance that a proper sample volume and/or reagent will be delivered in order to give a precise reported assay result. The precision and/or accuracy of delivered fluid volumes are typically known and often a significant contributor to the precision and accuracy of reported results.
- failure modes can include clots, excessively high viscosity and bubbles, all of which can cause imprecision and inaccuracy in delivered fluid volumes.
- failure modes can include clots, excessively high viscosity and bubbles, all of which can cause imprecision and inaccuracy in delivered fluid volumes.
- Some diagnostic analyzers use pressure detection systems to monitor the aspiration and dispense of sample and reagent liquids.
- many analyzers use flow-based systems and sensors other than pressure sensors to monitor flow rate for aspirating and dispensing a fluid. Based on research and math models it has been determined that electrical signals for flow monitoring within incompressible fluids creates a significant amount of noise and are not robust for failure detection. Pressure monitoring of compressible fluids and vision based monitoring systems yield more reliable failure detection capabilities.
- test volumes are under constant pressure to be reduced.
- liquid handling system requirements for precision and accuracy are becoming more stringent. Small deviations in delivered volume of liquid have a direct affect on the reaction and result.
- Some known art describes detecting liquid handling error modes such as bubbles, clots and foam as well as predicting that an aspirated volume is insufficient to allow reporting of a result. See, e.g., U.S. Pat. Nos.
- EP 608425 discloses a device for measuring viscosity of liquids.
- U.S. Pat. No. 5,257,529 discloses a method and device for the measurement of viscosity of liquids.
- WO 94/23280 discloses a method for the measurement of the surface tension of biological fluids.
- U.S. Pat. No. 4,165,632 discloses a method and apparatus for measuring the fluidity of liquids.
- U.S. Pat. No. 5,494,639 discloses a biosensor for measuring changes in viscosity and/or density of a fluid.
- the present invention is directed to a method that solves the foregoing problems improving the precision and accuracy of a metered fluid and to accurately detect failure modes in a metered fluid.
- One aspect of the invention is directed to a method for improving the accuracy or precision of a metered fluid.
- the method includes: estimating a fluidic property of the fluid being metered; and adjusting one or more control parameters based on the estimated property to improve the accuracy or precision of a metered fluid.
- the estimating the property of the fluid being metered includes: monitoring a physical event during the metering operation to collect sensed data; extracting features from the sensed data; and using the features to estimate fluid properties.
- the estimated fluidic property is viscosity and the sensed data is a pressure profile during a fluid aspiration.
- the method includes a diagnostic analyzer which includes a metering probe having a hard probe or a probe having a disposable tip and wherein the fluid is a body fluid sample.
- Another aspect of the invention provides a method for improving the detection of a failure mode.
- the method includes: estimating a fluidic property of the fluid being metered; and adjusting one or more control parameters or thresholds that determine when an error is flagged based on the estimated property to improve the detection of the failure.
- the estimating the fluidic property of the fluid being metered includes: monitoring a physical event during the metering operation to collect sensed data; extracting features from the sensed data; and using the features to estimate fluidic properties.
- the estimated fluidic property is viscosity and the sensed data is a pressure profile during a fluid aspiration.
- FIG. 1 is a flowchart showing a general outline of the present invention.
- FIG. 2 is a graph showing sample aspirate pressure profiles according to a preferred embodiment of the present invention.
- FIGS. 3A-3D show the stages of a fluid column elongating and breaking after dispense onto a dry chemistry slide according to a preferred embodiment of the present invention.
- FIG. 4 is a pressure profile of sample dispense to a slide.
- FIG. 5 is a depiction of a relationship between a mass/spring/damper of a mechanical system and a liquid in a dispensing probe tip according to a preferred embodiment of the present invention.
- FIGS. 6A and 6B are pressure profiles in relative A/D values showing two different viscosity fluids according to a preferred embodiment of the present invention. (Relative A/D values can be traced back to scientific units of pressure through simple linear transfer functions.)
- FIG. 7 shows the relationship between surface tension and viscosity according to a preferred embodiment of the present invention.
- FIG. 8 is a graph showing the effect of viscosity on volumetric error according to a preferred embodiment of the present invention.
- FIG. 9 is a graph showing relative error of aspiration volume as a function of viscosity according to a preferred embodiment of the present invention.
- FIG. 10 is a graph showing relative error for different aspiration volumes before any compensation, after volume-based compensation and after viscosity compensation according to a preferred embodiment of the present invention.
- FIG. 11 is a graph showing different templates developed according to the present invention used in matching fluid column detachments as a function of viscosity according to a preferred embodiment of the present invention.
- FIG. 12 shows a schematic diagram of a perfusion detection algorithm according to a preferred embodiment of the present invention which uses the templates of FIG. 11 .
- the present invention relies on estimating fluidic properties of a liquid, i.e., the property of the fluid, and using the knowledge of the fluidic properties to improve precision and accuracy of many metering operations. For example, in one instance a sample aspirate bias (i.e., imprecision) was found to be coupled to the fluidic property of viscosity. Based upon this knowledge a compensation function was implemented to resolve the bias.
- a compensation function is something that creates an adjustment to an output, parameter or control based on other information around it. Typical compensation functions use things such as linear combiners, which are well known in the art.
- the knowledge of fluidic properties can be used to determine failure modes in a metering operation.
- fluid properties is defined as a characteristic of a fluid, such as viscosity, surface tension, density, polarity, ionic characteristics, etc.
- metering is defined as the operation of aspirating or dispensing a fluid in a metering vessel in which a fluid is pulled into the vessel to be dispensed at a later time.
- the metering vessel is a hard probe (i.e., a probe that is not disposable and is typically washed after each use or a probe having a disposable tip.
- Metering generally (but not always) requires a pump, e.g., a piston pump, for aspirating and dispensing a fluid.
- failure mode is defined as an undesirable event that occurs during a fluid handling process, such as aspiration.
- a failure mode can include aspirating a bubble, clot or fibrin(s), perfusion, or being at the wrong height for dispense.
- FIG. 1 A generic overview of the present invention is shown in FIG. 1 .
- sensors such as pressures transducers, flow sensors, CCD, capacitive/current/resistance and ultrasonic to name a few sense or record physical events.
- a “physical event” is a fluid handling event, such as aspirating, dispensing, contacting the fluid, moving the fluid, e.g., with a tip or probe.
- Monitoring the physical event results in the creation of collected sensed data, such as the creation of a pressure profile. Data from the monitored physical events is collected and certain features are ascertained from them to be used for further analysis.
- Data can include recorded pressure signals within a control volume, flow signals through a tube, distance signals between a source and a target (one or both can be in motion), images or recorded video sequences of events, etc.
- a “feature” is defined as a measurable or estimable quantity/property that makes two events distinct from each other. Examples include column breaks (described below), and amplitude and frequency of pressure profiles. These features are subsequently used in algorithms known in the signal processing art to estimate a fluidic property, such as viscosity. Additional algorithms are used with the fluidic properties to:
- the sample viscosity is estimated from a few points in the pressure profile.
- the current method to estimate viscosity is based on a heuristic model.
- To estimate viscosity calibration curves are first developed based on fluids having known viscosities.
- the fluid with the unknown viscosity e.g., sample being analyzed, is then aspirated and the pressure profile is plotted.
- the pressure profile is the collected sensed data of the fluid. Based on a weighted sum of several points (i.e., features), a viscosity estimate is obtained for the unknown fluid.
- FIG. 2 shows pressure profiles for different viscosity fluids.
- the x-axis is relative time and the y-axis in relative pressure in A/D counts, wherein A/D counts are analog/digital counts as is known in the art.
- A/D counts are analog/digital counts as is known in the art.
- FIG. 2 shows, at the beginning of the plot, there 4 bands of profiles. These correspond to the 4 fluid viscosities used in this test. The four fluid viscosities were 2, 4, 8 and 12 cp. The different length profiles in each of the bands, correspond to different volumes of the same viscosity fluids. Once the viscosity has been determined, it will be used as described below.
- similar fluids having range of known properties e.g., viscosity
- Sensed data from metering events of known viscosities are plotted and coefficients for future viscosity estimation are obtained, which can then be used to estimate the viscosity of the unknown fluid.
- FIGS. 3A-3D show an enlarged view of a tip on the end of a metering probe shown as reference numeral 10 .
- the tip has just dispensed an amount of fluid 20 onto a fluid receiving surface 30 , such as a dry chemistry slide.
- the fluid joins the probe tip and slide at the end of the dispense process. As the tip begins to lift, the fluid will start to neck.
- Arrow 31 shows the force of fluid being pulled out of the tip.
- Arrows 32 and 33 show equal forces of surface tension acting opposite of each other. These forces tend to keep the fluid column intact.
- FIG. 4 show pressure profiles for several dispenses from a probe tip to a slide.
- a small vacuum is created because the fluid is being slightly pulled out of the tip as the fluid column stretches.
- Arrow 42 shows the necking of the fluid column as illustrated in FIGS. 3B and 3C .
- Arrow 43 shows the break of the column as illustrated in FIG. 3D .
- Arrow 44 shows a second order response to the fluid column break.
- the pressure profile from the fluid column break to the end of fluid recovery is very similar to a differential equation second order response.
- parameters in the math model can be shown to relate to viscosity and surface tension.
- the following equation describes the standard Laplace second order response.
- H ( s ) s 2 +2 ⁇ ⁇ n s+ ⁇ n 2 (1)
- the system dampening is represented by ⁇ and is highly coupled to, i.e. dependent on, viscosity.
- ⁇ n is the natural frequency and related to the surface tension of the fluid.
- the surface tension of the fluid acts similarly to a spring in a mass/damper/spring mechanical system. The relationship is illustrated in FIG. 5 .
- FIG. 5 shows the similarities in the response to a stimulus between a ball of fluid stretched outside a disposable tip, and the mechanical system on the right.
- the fluid viscosity and walls within the tip act similar to the damper on the right.
- Surface tension of the fluid/air interface on the drop and within the tip is similar to the spring on the right.
- the fluid mass inside and out of the tip is similar to the mass on the right.
- the backpressure caused by the surface tension after the break, along with the vacuum from the stretched fluid loads the system on the left similarly to displacing and holding the mass in the system on the right.
- the response of the fluid mass after the column break on the left is similar to releasing the stretched mass on the right.
- FIGS. 6A and 6B are similar to FIG. 4 and shows pressure profile for fluid column separation of a fluid from a probe tip dispenses onto a work surface such as a dry chemistry slide.
- FIG. 6A represents a lower viscosity fluid
- FIG. 6B represents a higher viscosity fluid.
- Arrow 51 represents the phase of each of the fluids.
- Arrow 52 represents the natural frequency of each of the fluids after the break of the fluid column.
- Arrow 53 represents the amplitude of the oscillation of the fluid.
- FIG. 7 This relationship is shown schematically in FIG. 7 .
- FIG. 7 indicates the greater the viscosity of the fluid, the less effect the surface tension will have on the properties of the fluid during metering operations such as an aspiration or dispense. This relationship can be used in error mode detection as described in greater detail below.
- Aspiration of bubbles by a metering probe or tip during aspiration of the desired fluid can affect the precision and accuracy of the amount of fluid aspirated and ultimately dispensed.
- the estimated viscosity as described above can be used as an additional feature to estimate bubble size and thus provide the actual amount of fluid that is dispensed.
- the plot shown in FIG. 8 shows a relationship between a select feature and the known aspirated error volume.
- the select feature is the predicted bubble time referenced from the point when the pump is turned off. This corresponds to the time difference between the start of a bubble aspiration and the known mechanical position of the pump piston, such as the time the pump is turned off.
- ground truth error volume in evaluating algorithm performance.
- Ground Truth describes a set of values that one has confidence describes the reality of a situation. Very frequently “ground truth” is acquired from other sources during development which are inaccessible in production applications. In this example, the disposable tips were weighed before and after aspiration, and with knowledge of the fluid density, the volume that was aspirated was determined. These values are considered “ground truth”, and are used for both training calibration functions as well as evaluating algorithm performance.
- color represents the estimated viscosity as determined above.
- FIG. 8 shows the viscosity trend is perpendicular (orthogonal) to the relationship between the bubble start index and the ground truth volume error. That is, for the same volume error (i.e., bubble size), higher viscosity fluids (represented by shading in FIG. 8 ) had a lower bubble start index.
- the viscosity, bubble break index and ground truth volume error one can create a better estimator for dispensed volume error by using both of these features in a linear combiner. Based on the estimated bubble size, we can specifically modify the number of pump steps taken in dispense, and recover what would have been a failed dispense event with improved accuracy or precision of the metered fluid.
- the relative error is decreased significantly after both volume based compensation and viscosity based compensation which uses the estimated viscosity as described above.
- the dots indicated no compensation the dots represent volume-based compensation and the dots represent both volume-based and viscosity compensation.
- Viscosity compensation is performed by a utilizing the viscosity estimate in an equation whose output is a modification to the requested metered volume. By metering this new requested volume, the actual metered volume will have better precision and accuracy relative to the original requested volume.
- perfusion occurs when sample is not successfully dispensed to a target media, such as a dry slide.
- perfusion is defined as an event where fluid hanging from a metering probe or a disposable tip of a metering probe runs up the side of the tip/probe. The net result is that the assay is run without sample or insufficient sample, leading to erroneous results or an error message.
- Example fluid column detachments can be seen in pressure profiles in FIGS. 4 , 6 A and 6 B.
- FIGS. 6A and 6B show the differences in the shape of the pressure profile after detachment as a function of viscosity. The response changes from under-damped in low viscosity fluids to damped in higher viscosity fluids.
- a matched filter as known in the art is obtained by correlating a known signal, or template, with an unknown signal to detect the presence of the template in the unknown signal. This is equivalent to convolving the unknown signal with a time-reversed version of the template (cf. convolution).
- the matched filter is the optimal linear filter for maximizing the signal to noise ratio (SNR) in the presence of Gaussian white noise.
- Matched filters are commonly used in radar, in which a signal is sent out, and one measures the reflected signals, looking for something similar to what was sent out.
- Pulse compression is an example of matched filtering.
- Two-dimensional matched filters are commonly used in image processing, e.g., to improve SNR for X-ray pictures.
- matched filtering analyzes the correlation between the pressure signal of the fluid being dispensed and a template used in the matched filter.
- the template is a signal that describes the expected response trying to be matched.
- the template is developed from pressure profiles with fluids having known or estimated viscosities and surface tensions. Surface tension can be estimated as described above. The estimated or actual viscosity information is used to modify the templates as shown in FIG. 11 .
- FIG. 11 span expected pressure signature responses in fluid column detachment for viscosities 4 through 10 cp. As the viscosity gets higher, the exponential changes, as well as the amplitude. Changes to the shape of the template through usage of viscosity compensation allow this algorithm to function well beyond the expected operating range of input fluid properties.
- FIG. 12 shows a high level overview how the perfusion algorithm works.
- the algorithm takes the estimated viscosity of the fluid being dispensed as determined above and uses that to generate a template similar to the templates in FIG. 11 .
- the template is generated by an optimization against some curves experimentally derived to determine best fit coefficients, so that later one can simply plug in viscosities, run a function and generate a template.
- the template 51 is compared to a section or window 52 of the pressure profile 53 similar to that shown in FIG. 4 and analyzed using correlation to determine if the pressure profile at that increment fits the template. Then the window is moved further down, and another correlation occurs.
- the template is moved along the pressure profile and it searches for a correlation.
- the maximum correlation value is thresholded, and index retained.
- the maximum correlation exceeds a predetermined threshold, then the dispense occurred without perfusion. In other words if there is an increment of the pressure profile that matches the template to within a predetermined correlation, the dispense was successful and no perfusion occurred. Otherwise a perfusion error is logged.
- the index of maximum correlation is what can be fed into the algorithm to detect phase in surface tension estimation described in a previous section. Knowing the surface tension of a fluid, one can take that fluidic property and compare it to different dispensed volumes to look for trends/bias attributed to surface tension and build a correction for it to yield a more accurate dispense. Likewise, one can see if surface tension is correlated to features that are used to predict failure modes such as bubbles during aspiration. For example, bubbles have an air/fluid interface, and the higher the surface tension, the stronger the signal amplitude during the bubble entrance into the tip. One can adjust thresholds that look for bubble errors as a function of surface tension.
- Estimated fluidic properties can also be used to improve the sensitivity and/or specificity of failure modes.
- sensitivity is defined as the probability of detecting something, i.e., a failure mode.
- specificity is defined as the probability that what was detected, i.e., the failure mode, is in fact, correct. That is, specificity is the probability that the failure mode detected is correct and not a false alarm.
- estimating the fluidic property of surface tension as described above can be used to improve the sensitivity and/or specificity of error modes. This is particularly the case with lower viscosity fluids.
- a “lower viscosity fluid” is defined as a fluid having a viscosity of ⁇ 3.5 centipoises (cp), more preferably ⁇ 2.5 cp.
- Particularly preferred lower viscosity fluids are patient serum samples having a viscosity of 1.5 to 2.5 cp.
- higher viscosity fluids tend to dampen the effect of surface tension. Accordingly, surface tension will play a smaller role in failure modes in higher viscosity fluids.
- the method for improving accuracy and precision of a metered fluid and in detecting error modes according to the present invention can be implemented by a computer program, having computer readable program code, interfacing with the computer controller of the analyzer as is known in the art.
Abstract
Description
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- 1) Modify control parameters to which control the dispense flow rate, height, number of pump steps displaced, etc. used in controlling fluid delivery of a particular fluid and improve precision and accuracy of delivered volumes.
- 2) Improve failure detection capabilities monitoring error modes on subsequent fluid deliveries using the same fluid.
H(s)=s 2+2ξ
The system dampening is represented by ξ and is highly coupled to, i.e. dependent on, viscosity. ωn is the natural frequency and related to the surface tension of the fluid. The surface tension of the fluid acts similarly to a spring in a mass/damper/spring mechanical system. The relationship is illustrated in
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